By Topic

Genetic algorithms in stochastic optimisation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Sanabria, L.A. ; Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Bundoora, Vic., Australia ; Soh, B. ; Dillon, T.S. ; Chang, L.

Genetic algorithms (GA) have been successfully used in a variety of optimisation problems. They are especially strong in the solution of difficult problems, which cannot be solved or are hard to solve using conventional linear or nonlinear optimisation. One of those problems is the constrained stochastic optimisation (CSO) problem. The central characteristic of these kinds of problems is that some or all variables of the problem are given in the form of random variables. Random variables capture the uncertainties associated with system behaviour. These kinds of variables must be used whenever the problem parameters fluctuate within very large range of values and/or it is difficult to assess their expected values. Problems of this type arise in a variety of engineering fields, in power systems, transport engineering, Internet access, communication networks, etc. In these and many other areas, the system has to be designed for mid to long-term optimum operation forcing the design engineer to use CSO models. Solution of the CSO problem using conventional methods is very complicated. Genetic algorithms offer simple yet accurate solutions using computer efficient techniques. To illustrate the method, the problem of finding the optimum design of an Intranet server is solved.

Published in:

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on  (Volume:2 )

Date of Conference:

8-12 Dec. 2003